We report the study of the accretion disk structure of the period bouncer cataclysmic variable EZ Lyn (SDSS J080434.20+510349.2) in quiescence based on our new time-resolved photometric and spectroscopic observations and data extracted from archives. The object magnitude now is V = 17.95(5), close to its brightest before the first superoutburst in 2006. We confirmed the presence of the small eclipse in the optical light curve. The spectra obtained in quiescence at different epochs look similar. However, the contribution of the disk and intensities of emission lines are strongly varied. We singled out pure accretion disk spectra and found that the Balmer decrement Hα:Hβ:Hγ:Hδ = 1.61:1.0:0.76:0.59 is comparable with one at bright accretion disks in longer period cataclysmic variables. The decrement suggests that emission lines are excited collisionally in an optical thin part of the disk with average density and temperature of logN ₀ = 12.5(2) and T = (10–15) × 10³ K. Based on the photometric data and our modeling techniques, we redetermined the mass of M _WD = 0.85(1) M _⊙ and the current effective temperature T _WD,eff = 11,250(50) K of the white dwarf. The secondary has mass of M ₂ = 0.042(14) M _⊙. The system inclination is 79.0°(2). The mass accretion rate is about M ̇ ≈ ( 0.3 – 3.0 ) × 10 − 12 M ⊙ yr⁻¹. The disk luminosity, together with results from light-curve modeling, suggests a low effective temperature ∼2500 K of the continuum emitting region, where also the spiral arm pattern is hosted.

Early detection of earthquakes is essential for minimizing potential damage and ensuring public safety. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), provide a promising alternative for analyzing seismic waves. In contrast, traditional methods such as the short-term average/long-term average (STA/LTA) algorithm and the Akaike information criterion (AIC) have limitations in detecting primary (P) waves in high-noise conditions, caused by industrial and anthropogenic disturbances. This study presents a CNN-based automatic P-wave detection model tailored for the Almaty city region. The seismic dataset used in this research was obtained from the IRIS database and includes data collected from seven stations within a 333 km radius of Almaty, Kazakhstan. The proposed model achieves a recall rate of 89.1% and an accuracy of 94.1% compared to other deep learning-based models. Experimental results demonstrate that this method enhances the reliability of automatic early earthquake warning systems and improves the accuracy of P-wave detection. The research outputs presented for the local region are unique. Applying CNNs in seismic monitoring facilitates the development of efficient automated systems that minimize risks and improve response measures for natural disasters.

In the current study, porous silicon (por-Si) samples were fabricated by electrochemical etching at different times (20 min, 40 min, 60 min). Scanning electron microscope (SEM) images of horizontal cross-sections of the samples showed the formation of pores. The etched samples’ porosity was determined by the gravimetric method and amounted to 59.5%, 72.7%, 83.3%, respectively. Optical characteristics such as Raman spectra and photoluminescence (PL) spectra were obtained. The current-voltage and capacitance-voltage characteristics were also measured to calculate the sensitivity of the samples. The study results show that sample, which is etched for 40 minutes has a maximum response value to ammonia (NH₃) gas than others, and the sensitivity is 33.25. The results demonstrated that it is possible to develop a high sensitive sensor device based on por-Si for determining NH₃ gas in concentrations below 0.1 ppm at room temperature.

Aims. We investigate the role of selective dissociation in the process of star formation by comparing the physical parameters of protostellar-prestellar cores and the selected regions with the CO isotope distributions in photo-dissociation regions. We seek to understand whether there is a better connection between the evolutionary age of star forming regions and the effect of selective dissociation Methods. We used wide-field observations of the ¹²CO, ¹³CO, and C¹⁸O (J = 1–0) emission lines to study the ongoing star formation activity in the Aquila molecular region, and we used the 70 and 250 μm data to describe the heating of the surrounding material and as an indicator of the evolutionary age of the core. Results. The protostellar-prestellar cores are found at locations with the highest C¹⁸O column densities and their increasing evolutionary age coincides with an increasing 70μm/250μm emission ratio at their location. The evolutionary age of the cores may also follow from the ¹³CO versus C¹⁸O abundance ratio, which decreases with increasing C¹⁸O column densities. The original mass has been estimated for nine representative star formation regions and the original mass of the region correlates well with the integrated 70 μm flux density. Similarly, the X_¹³CO/X_C¹⁸O ratio, which provides the dissociation rate for these regions correlates with the 70 μm/250 μm flux density ratio and reflects the evolutionary age of the star formation activity.

The present article studied gas sensor sensing characteristics based on surface-modified porous silicon (PS) by depositing the metal oxide semiconductor layer. The PS layer was prepared through the electrochemical etching of crystalline silicon in an HF-based solution. DC magnetron sputtering technology was used to obtain the p-CuO layer on the surface of the p-PS. The obtained material’s structural, morphological, and sensing behavior were investigated using SEM, XRD, Raman spectra, and the current–voltage characteristics. For the detection of toluene and chloroform vapors, a planar structure was used. The sensing response value revealed that the CuO/PS-based gas sensors have good sensitivity for toluene and chloroform vapors. The sensing mechanism is explained using schematic energy band diagrams. Therefore, this approach is helpful for the development of a simple, cost-effective sensor for detecting non-polar chemical analytes.